A Cloud-Based Technology for Conducting In-class Exercises in Data Science and Machine Learning Courses
Abstract
Teaching data science can be challenging partly due to a diverse student population and the difficulty of providing a hands-on coding experience on complex topics. To address these challenges, we introduce a software package that facilitates active learning in the form of in-class coding exercises. This approach provides a much-needed hands-on experience in courses with a diverse student population and a highly technical content. Utilizing a popular cloud-based technology, JupyterHub, this approach enables in-class exercises with personalized feedback from the instructor. We report a classroom experience of using the technology for the first time in a graduate-level Machine Learning course, consisting of a mix of Data Science and Computer Science students. We found that, to a great extent, the course instructor could conduct complex in-class exercises within 10-15 minutes of class time. The instructor was able to understand students' abilities and challenges better and provide them with meaningful personalized feedback as well as group feedback. Students felt that the experience provided valuable hands-on practice, helped them figure out coding mistakes, and prepared them better for homework assignments.
Publication Title
SIGCSE 2023 - Proceedings of the 54th ACM Technical Symposium on Computer Science Education
Recommended Citation
Pahi, K., & Phan, V. (2023). A Cloud-Based Technology for Conducting In-class Exercises in Data Science and Machine Learning Courses. SIGCSE 2023 - Proceedings of the 54th ACM Technical Symposium on Computer Science Education, 1, 868-874. https://doi.org/10.1145/3545945.3569838
